Sample technique or sampling method
In Statistics, the sampling method or sampling
technique is the process of studying the population by gathering
information and analyzing those data. It is the basis of the data where the sample space is enormous. The statistical
research is of two forms:
- In
the first form, each domain is studied, and the result can be obtained by
computing the sum of all units.
- In
the second form, only a unit in the field of the survey is taken. It
represents the domain. The result of these samples extends to the domain.
This type of study is known as the sample survey.
In this article, let us discuss the different sampling methods in
research such as probability sampling and non-probability sampling methods and
various methods involved in those two approaches in detail.
What are the
sampling methods?
There are several different sampling techniques available, and they can
be subdivided into two groups. All these methods of sampling may involve
specifically targeting hard or approach to reach groups.
Types of Sampling
Method
In Statistics, there are different sampling techniques available to get
relevant results from the population. The two different types of sampling
methods are::
- Probability
Sampling
- Non-probability
Sampling
What is Probability
Sampling?
The probability sampling method utilizes some form of random selection.
In this method, all the eligible individuals have a chance of selecting the
sample from the whole sample space. This method is more time consuming and
expensive than the non-probability sampling method. The benefit of using
probability sampling is that it guarantees the sample that should be the
representative of the population.
Probability
Sampling Types
Probability Sampling methods are further classified into different
types, such as simple random sampling, systematic sampling, stratified
sampling, and clustered sampling. Let us discuss the different types of
probability sampling methods along with illustrative examples here in
detail.
Simple Random
Sampling
In simple random sampling technique, every item in the population has an
equal and likely chance of being selected in the sample. Since the item
selection entirely depends on the chance, this method is known as “Method of
chance Selection”. As the sample size is large, and the item is chosen
randomly, it is known as “Representative Sampling”.
Example:
Suppose we want to select a simple random sample of 200 students from a
school. Here, we can assign a number to every student in the school database
from 1 to 500 and use a random number generator to select a sample of 200
numbers.
Systematic Sampling
In the systematic sampling method, the items are selected from the
target population by selecting the random selecting point and selecting the
other methods after a fixed sample interval. It is calculated by dividing the
total population size by the desired population size.
Example:
Suppose the names of 300 students of a school are sorted in the reverse
alphabetical order. To select a sample in a systematic sampling method, we have
to choose some 15 students by randomly selecting a starting number, say
5. From number 5 onwards, will select every 15th person from the sorted
list. Finally, we can end up with a sample of some students.
Stratified Sampling
In a stratified sampling method, the total population is divided into
smaller groups to complete the sampling process. The small group is formed
based on a few characteristics in the population. After separating the
population into a smaller group, the statisticians randomly select the sample.
For example, there are three bags (A, B and C), each with
different balls. Bag A has 50 balls, bag B has 100 balls, and bag C has 200
balls. We have to choose a sample of balls from each bag proportionally.
Suppose 5 balls from bag A, 10 balls from bag B and 20 balls from bag C.
Clustered Sampling
In the clustered sampling method, the cluster or group of people are
formed from the population set. The group has similar significatory
characteristics. Also, they have an equal chance of being a part of the sample.
This method uses simple random sampling for the cluster of population.
Example:
An educational institution has ten branches across the country with
almost the number of students. If we want to collect some data regarding
facilities and other things, we can’t travel to every unit to collect the
required data. Hence, we can use random sampling to select three or four
branches as clusters.
All these four methods can be understood in a better manner with the
help of the figure given below. The figure contains various examples of how
samples will be taken from the population using different techniques.
What is
Non-Probability Sampling?
The non-probability sampling method is a technique in which the
researcher selects the sample based on subjective judgment rather than the
random selection. In this method, not all the members of the population have a
chance to participate in the study.
Non-Probability
Sampling Types
Non-probability Sampling methods are further classified into different
types, such as convenience sampling, consecutive sampling, quota sampling,
judgemental sampling, snowball sampling. Here, let us discuss all these types
of non-probability sampling in detail.
Convenience
Sampling
In a convenience sampling method, the samples are selected from the
population directly because they are conveniently available for the researcher.
The samples are easy to select, and the researcher did not choose the sample
that outlines the entire population.
Example:
In researching customer support services in a particular region, we ask
your few customers to complete a survey on the products after the purchase.
This is a convenient way to collect data. Still, as we only surveyed customers
taking the same product. At the same time, the sample is not representative of
all the customers in that area.
Consecutive
Sampling
Consecutive sampling is similar to convenience sampling with a slight
variation. The researcher picks a single person or a group of people for
sampling. Then the researcher researches for a period of time to analyze the
result and move to another group if needed.
Quota Sampling
In the quota sampling method, the researcher forms a sample that
involves the individuals to represent the population that is based on specific
traits or qualities. The researcher chooses the sample subsets that bring the
useful collection of data that generalizes the entire people.
Purposive or
Judgmental Sampling
In purposive sampling, the samples are selected only based on the
researcher’s knowledge. As their knowledge is instrumental in creating the
samples, there are the chances of obtaining highly accurate answers with a
minimum marginal error. It is also known as judgmental sampling or
authoritative sampling.
Snowball Sampling
The snowball sampling is also known as chain-referral sampling
technique. In this method, the samples have traits that are difficult to find.
So, each identified member of a population is asked to find the other sampling
units. Those sampling units also belong to the same targeted population.
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